Ridge regression is a method we can use to fit a regression model when multicollinearity is present in the data.

In a nutshell, least squares regression tries to find coefficient estimates that minimize the sum of squared residuals (RSS):

**RSS = Σ(y _{i} – ŷ_{i})2**

where:

**Σ**: A greek symbol that means*sum***y**: The actual response value for the i_{i}^{th}observation**ŷ**: The predicted response value based on the multiple linear regression model_{i}

Conversely, ridge regression seeks to minimize the following:

**RSS + λΣβ _{j}^{2}**

where *j* ranges from 1 to *p* predictor variables and λ ≥ 0.

This second term in the equation is known as a *shrinkage penalty*. In ridge regression, we select a value for λ that produces the lowest possible test MSE (mean squared error).

This tutorial provides a step-by-step example of how to perform ridge regression in Python.

**Step 1: Import Necessary Packages**

First, we’ll import the necessary packages to perform ridge regression in Python:

**import pandas as pd
from numpy import arange
from sklearn.linear_model import Ridge
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import RepeatedKFold**

**Step 2: Load the Data**

For this example, we’ll use a dataset called **mtcars**, which contains information about 33 different cars. We’ll use **hp** as the response variable and the following variables as the predictors:

- mpg
- wt
- drat
- qsec

The following code shows how to load and view this dataset:

**#define URL where data is located
url = "https://raw.githubusercontent.com/Statology/Python-Guides/main/mtcars.csv"
#read in data
data_full = pd.read_csv(url)
#select subset of data
data = data_full[["mpg", "wt", "drat", "qsec", "hp"]]
#view first six rows of data
data[0:6]
mpg wt drat qsec hp
0 21.0 2.620 3.90 16.46 110
1 21.0 2.875 3.90 17.02 110
2 22.8 2.320 3.85 18.61 93
3 21.4 3.215 3.08 19.44 110
4 18.7 3.440 3.15 17.02 175
5 18.1 3.460 2.76 20.22 105**

**Step 3: Fit the Ridge Regression Model**

Next, we’ll use the RidgeCV() function from sklearn to fit the ridge regression model and we’ll use the RepeatedKFold() function to perform k-fold cross-validation to find the optimal alpha value to use for the penalty term.

**Note:** The term “alpha” is used instead of “lambda” in Python.

For this example we’ll choose k = 10 folds and repeat the cross-validation process 3 times.

Also note that RidgeCV() only tests alpha values .1, 1, and 10 by default. However, we can define our own alpha range from 0 to 1 by increments of 0.01:

**#define predictor and response variables
X = data[["mpg", "wt", "drat", "qsec"]]
y = data["hp"]
#define cross-validation method to evaluate model
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
#define model
model = RidgeCV(alphas=arange(0, 1, 0.01), cv=cv, scoring='neg_mean_absolute_error')
#fit model
model.fit(X, y)
#display lambda that produced the lowest test MSE
print(model.alpha_)
0.99**

The lambda value that minimizes the test MSE turns out to be **0.99**.

**Step 4: Use the Model to Make Predictions**

Lastly, we can use the final ridge regression model to make predictions on new observations. For example, the following code shows how to define a new car with the following attributes:

- mpg: 24
- wt: 2.5
- drat: 3.5
- qsec: 18.5

The following code shows how to use the fitted ridge regression model to predict the value for *hp* of this new observation:

**#define new observation
new = [24, 2.5, 3.5, 18.5]
#predict hp value using ridge regression model
model.predict([new])
array([104.16398018])
**

Based on the input values, the model predicts this car to have an *hp* value of **104.16398018**.

You can find the complete Python code used in this example here.